{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synthetic Control\n",
    "\n",
    "## One Amazing Math Trick to Learn What can’t be Known\n",
    "\n",
    "When we looked at difference-in-difference, we had data on multiple customers from 2 different cities: Porto Alegre and Florianópolis. The data span 2 different time periods: before and after a marketing intervention was done in Porto Alegre to boost customer deposits. To estimate the treatment effect, we ran a regression that gave us the difference-in-difference estimator and its standard error. \n",
    "\n",
    "For that case, we had a lot of samples, because data was disaggregated. But what if all we have is aggregated data on the city level? For instance, let's pretend all we have is the average level of deposits in both cities before and after the intervention.\n",
    "\n",
    "|city|before|after|\n",
    "|--|--|--|\n",
    "|FL|171.64|206.16|\n",
    "|POA|46.01|87.06|\n",
    "\n",
    "We would still be able to compute the Diff-in-Diff estimator \n",
    "\n",
    "$\n",
    "(E[Y(1)|D=1] - E[Y(1)|D=0]) - (E[Y(0)|D=1] - E[Y(0)|D=0]) = (87.06 - 206.16) - (46.01 - 171.64) = 6.53\n",
    "$\n",
    "\n",
    "However, note that the sample size here is 4, which is also the number of parameters in our Diff-in-Diff models. In this case, the standard error is not well defined, so what should we do? Another problem is that Florianopolis might not be as similar to Porto Alegre as we would want to. For instance, Florianopolis is known for its beautiful beaches and easy going people while Porto Alegre is more famous for its barbecue and the traditional gaucho. The problem is that you can't never know for sure if you are using an appropriate control group. \n",
    "\n",
    "To solve this problem, we will use what is known as [**\"the most important innovation in the policy evaluation literature in the last few years\"**](https://www.aeaweb.org/articles?id=10.1257/jep.31.2.3), Synthetic Controls. It is based on a simple, yet powerful idea. We don't need to find any single unit in the untreated that is very similar to the treated. Instead, we can forge our own as a combination of multiple untreated units, creating what is effectively a synthetic control. Synthetic control is so effective yet so intuitive that it even got an article published, not on a scientific journal, but on the [Washington Post](https://www.washingtonpost.com/news/wonk/wp/2015/10/30/how-to-measure-things-in-a-world-of-competing-claims/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import style\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 6)\n",
    "style.use(\"fivethirtyeight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see it in action, consider the problem of estimating the effect of cigarette taxation on its consumption. To give a bit of context, this is a question that had been debated for a long time in economics. One side of the argument says that taxes will increase the cost of cigars, which will lower its demand. The other side argues that since cigarettes cause addiction, change in their price won't change their demand by much. In economic terms, we would say that the demand for cigarettes is inelastic on price, and an increase in taxation is just a way to increase government income at the cost of smokers. To settle things, we will look at some US data regarding the matter.\n",
    "\n",
    "In 1988, California passed a famous Tobacco Tax and Health Protection Act, which became known as [Proposition 99](https://en.wikipedia.org/wiki/1988_California_Proposition_99). \"Its primary effect is to impose a 25-cent per pack state excise tax on the sale of tobacco cigarettes within California, with approximately equivalent excise taxes similarly imposed on the retail sale of other commercial tobacco products, such as cigars and chewing tobacco. Additional restrictions placed on the sale of tobacco include a ban on cigarette vending machines in public areas accessible by juveniles, and a ban on the individual sale of single cigarettes. Revenue generated by the act was earmarked for various environmental and health care programs, and anti-tobacco advertisements.\" \n",
    "\n",
    "To evaluate its effect, we can gather data on cigarette sales from multiple states and across a number of years. In our case, we got data from the year 1970 to 2000 from 39 states. Other states had similar Tobacco control programs and were dropped from the analysis. Here is what our data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>retprice</th>\n",
       "      <th>california</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>3</td>\n",
       "      <td>1970</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>3</td>\n",
       "      <td>1971</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>39.700001</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>3</td>\n",
       "      <td>1972</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>3</td>\n",
       "      <td>1973</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3</td>\n",
       "      <td>1974</td>\n",
       "      <td>126.699997</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year     cigsale   retprice  california  after_treatment\n",
       "62      3  1970  123.000000  38.799999        True            False\n",
       "63      3  1971  121.000000  39.700001        True            False\n",
       "64      3  1972  123.500000  39.900002        True            False\n",
       "65      3  1973  124.400002  39.900002        True            False\n",
       "66      3  1974  126.699997  41.900002        True            False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cigar = (pd.read_csv(\"data/smoking.csv\")\n",
    "         .drop(columns=[\"lnincome\",\"beer\", \"age15to24\"]))\n",
    "\n",
    "cigar.query(\"california\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have `state` as the state index, where California is the number 3. Our covariates are `retprice`, the cigarette retail price, and `cigsale`, the per-capita sales of cigarettes in packs. Our outcome variable of interest is `cigsale`. Finally, we have boolean helper variables to signal the state of California and the post intervention period. If we plot the sales of cigarettes for California and other states across time, this is what we would get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "(cigar\n",
    " .assign(california = np.where(cigar[\"california\"], \"California\", \"Other States\"))\n",
    " .groupby([\"year\", \"california\"])\n",
    " [\"cigsale\"]\n",
    " .mean()\n",
    " .reset_index()\n",
    " .pivot(\"year\", \"california\", \"cigsale\")\n",
    " .plot(ax=ax, figsize=(10,5)))\n",
    "\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Cigarette Sales Trend\")\n",
    "plt.title(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the time for which we have data, people in California apparently bought less cigarettes than the national average. Also, it appears to be a decreasing movement in cigarette consumption after the 80s. It looks like after Proposition 99 the decreasing trend accelerated for California, compared to other states, but we can't say that for sure. It is just a guess that we have by examining the plot. \n",
    "\n",
    "To answer the question of whether Proposition 99 had an effect on cigarette consumption, we will use the pre-intervention period to build a synthetic control. We will combine the other states to **build a fake state that resembles very closely the trend of California**. Then, we will see how this synthetic control behaves after the intervention. \n",
    "\n",
    "## We have Time\n",
    "\n",
    "To make matters a little bit more formal suppose that we have J+1 units. Without loss of generality, assume that unit 1 is the unit that gets affected by an intervention. Units \\\\(j=2,...,J+1\\\\) are a collection of untreated units that we will refer to as the \"donor pool\". Also assume that the data we have span T time periods, whith \\\\(T_0\\\\) periods before the intervention. For each unit j and each time t, we observe the outcome \\\\(Y_{jt}\\\\). For each unit j and period t, define \\\\(Y^N_{jt}\\\\) as the potential outcome without intervention and \\\\(Y^I_{jt}\\\\), the potential outcome with intervention. Then, the effect for the treated unit \\\\(j=1\\\\) at time t, for \\\\(t>T_0\\\\) is defined as \n",
    "\n",
    "$\n",
    "\\tau_{1t} = Y^I_{jt} - Y^N_{jt}\n",
    "$\n",
    "\n",
    "Since unit \\\\(j=1\\\\) is the treated one, \\\\(Y^I_{jt}\\\\) is factual but \\\\(Y^N_{jt}\\\\) is not. The challenge then becomes how do we estimate \\\\(Y^N_{jt}\\\\). Note how the treatment effect is defined for each period, which means it can change in time. It doesn't need to be instantaneous. It can accumulate or dissipate. To put it in a picture, the problem of estimating the treatment effect boils down to the problem of estimating what would have happened to the outcome of unit \\\\(j=1\\\\) if it had not been treated.\n",
    "\n",
    "![img](data/img/synth-control/synth_img.png)\n",
    "\n",
    "To estimate \\\\(Y^N_{jt}\\\\), we remember that a combination of units in the donor pool may approximate the characteristics of the treated unit much better than any untreated unit alone. Thus, a synthetic control is defined as a weighted average of the units in the control pool. Given the weights \\\\(\\pmb{W}=(w_2, ..., w_{J+1})\\\\), the synthetic control estimate of \\\\(Y^N_{jt}\\\\) is\n",
    "\n",
    "$\n",
    "\\hat{Y}^N_{jt} = \\sum^{J+1}_{j=2} w_j Y_{jt}\n",
    "$\n",
    "\n",
    "If all this math makes your head hurt, you are not alone. But don't worry, we got lots of examples to make it more intuitive. For once, I like to think about synthetic control as an upside down way of doing regression. As we know, linear regression is also a way of getting the prediction as a weighted average of the variables. Think about those regressions like the one in the diff-in-diff example where each variable is a dummy for a time period. In this case, regression can be represented as the following matrix multiplication\n",
    "\n",
    "![img](data/img/synth-control/regr_time.png)\n",
    "\n",
    "On the synthetic control case, we don't have lots of units, but we do have lots of time periods. So what we do is flip the input matrix around. Then, the units become the \"variables\" and we represent the outcome as a weighted average of the units, like in the following matrix multiplication.\n",
    "\n",
    "![img](data/img/synth-control/regr_space.png)\n",
    "\n",
    "If we have more than one feature per time period, we can pile up the features like this. The important thing is to make it so that the regression is trying to \"predict\" the treated unit 1 by using the other units. This way, we can choose the weights in some optimal way to achieve this proximity we want. We can even scale features differently to give different importance to them.\n",
    "\n",
    "![img](./data/img/synth-control/regr_space_x.png)\n",
    "\n",
    "So, if synthetic control can be viewed as a linear regression, it also means that we can estimate the weights with OLS right? Yup! In fact, let's do this now.\n",
    "\n",
    "## Synthetic Control as Linear Regression\n",
    "\n",
    "![img](./data/img/synth-control/allways.png)\n",
    "\n",
    "To estimate the treatment effect with synthetic control, we will try to build a \"fake unit\" that resembles the treated unit before the intervention period. Then, we will see how this \"fake unit\" behaves after the intervention. The difference between the synthetic control and the unit that it mimics is the treatment effect.\n",
    "\n",
    "To do this with linear regression, we will find the weight using OLS. We will minimise the square distance between the weighted average of the units in the donor pool and the treated unit for the pre-intervention period.\n",
    "\n",
    "To do so, the first thing we need is to convert the units (in our case, the states) into the columns and the time into the rows. Since we have 2 features, `cigsale` and `retprice`, we will pile them on top of each other like we did in the picture above. We will build a synthetic control that looks a lot like California in the pre intervention period and see how it would behave in the post intervention period. For this reason, it is important that we select only the pre-intervention period. Here, the features seem to be on a similar scale, so we won't do anything to them. If features are in different scales, one in the thousands and another in the decimals, the bigger feature will be the most important when minimizing the difference. To avoid this, it's important to scale them first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">cigsale</th>\n",
       "      <th>1970</th>\n",
       "      <td>89.800003</td>\n",
       "      <td>100.300003</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>106.400002</td>\n",
       "      <td>132.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>95.400002</td>\n",
       "      <td>104.099998</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>111.500000</td>\n",
       "      <td>105.400002</td>\n",
       "      <td>131.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>101.099998</td>\n",
       "      <td>103.900002</td>\n",
       "      <td>123.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>108.800003</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>102.900002</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>116.599998</td>\n",
       "      <td>109.500000</td>\n",
       "      <td>141.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>108.199997</td>\n",
       "      <td>109.699997</td>\n",
       "      <td>126.699997</td>\n",
       "      <td>...</td>\n",
       "      <td>119.900002</td>\n",
       "      <td>111.800003</td>\n",
       "      <td>145.800003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "state                 1           2           3   ...          37          38  \\\n",
       "        year                                      ...                           \n",
       "cigsale 1970   89.800003  100.300003  123.000000  ...  114.500000  106.400002   \n",
       "        1971   95.400002  104.099998  121.000000  ...  111.500000  105.400002   \n",
       "        1972  101.099998  103.900002  123.500000  ...  117.500000  108.800003   \n",
       "        1973  102.900002  108.000000  124.400002  ...  116.599998  109.500000   \n",
       "        1974  108.199997  109.699997  126.699997  ...  119.900002  111.800003   \n",
       "\n",
       "state                 39  \n",
       "        year              \n",
       "cigsale 1970  132.199997  \n",
       "        1971  131.699997  \n",
       "        1972  140.000000  \n",
       "        1973  141.199997  \n",
       "        1974  145.800003  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [\"cigsale\", \"retprice\"]\n",
    "\n",
    "inverted = (cigar.query(\"~after_treatment\") # filter pre-intervention period\n",
    "            .pivot(index='state', columns=\"year\")[features] # make one column per year and one row per state\n",
    "            .T) # flip the table to have one column per state\n",
    "\n",
    "inverted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can define our Y variable as the state of California and the X as the other states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = inverted[3].values # state of california\n",
    "X = inverted.drop(columns=3).values  # other states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we run a regression. Having an intercept is equivalent to adding another state where every row is 1. You can do that, but I think it's more complicated and I'll just leave it out. The regression will return the set of weights that minimize the square difference between the treated unit and the units in the donor pool. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.436, -1.038,  0.679,  0.078,  0.339,  1.213,  0.143,  0.555,\n",
       "       -0.295,  0.052, -0.529,  1.235, -0.549,  0.437, -0.023, -0.266,\n",
       "       -0.25 , -0.667, -0.106, -0.145,  0.109,  0.242, -0.328,  0.594,\n",
       "        0.243, -0.171, -0.02 ,  0.14 , -0.811,  0.362,  0.519, -0.304,\n",
       "        0.805, -0.318, -1.246,  0.773, -0.055, -0.032])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "weights_lr = LinearRegression(fit_intercept=False).fit(X, y).coef_\n",
    "weights_lr.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These weights show us how to build the synthetic control. We will multiply the outcome of state 1 by -0.436, of state 2 by -1.038, of state 4 by 0.679 and so on. We can achieve this with a dot product between the matrix from the states in the pool and the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "calif_synth_lr = (cigar.query(\"~california\")\n",
    "                  .pivot(index='year', columns=\"state\")[\"cigsale\"]\n",
    "                  .values.dot(weights_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our synthetic control, we can plot it with the outcome variable of the State of California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"], label=\"California\")\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], calif_synth_lr, label=\"Synthetic Control\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK… Something seems off. What grabs your attention in this picture? First, after the intervention, the synthetic control  has more cigarette sales than California. This is an indicative that the intervention was successful in lowering cigarette demand. Second, notice how the pre-intervention period is fitted perfectly. The synthetic control is able to match the state of California exactly. This is a sign that our synthetic control model is probably overfitting the data. Another sign of this is the huge variance on the outcome variable of the synthetic control after the intervention. Notice how it doesnt follow smooth patterns. Instead, it goes up and down and up and down. \n",
    "\n",
    "![img](./data/img/synth-control/out-of-sample.png)\n",
    "\n",
    "If we think about why this is happening, remember that we have 38 states in our donor pool. So our linear regression has 38 parameters to play with in order to make the pretreatment pool match the treatment as close as it can. This is the case where even if T is large, N is also large, which gives too much flexibility to our linear regression. If you are familiar with regularized models, know that you could use Ridge or Lasso regression to fix this. Here, we will look at another more traditional way to avoid overfitting.\n",
    "\n",
    "## Don't Extrapolate\n",
    "\n",
    "Suppose you have data like in this table below and are asked to build a synthetic control to reproduce the treated unit using any linear combination of the control units.\n",
    "\n",
    "|unit|sales|price|\n",
    "|--|--|--|\n",
    "|control 1|8|8|\n",
    "|control 2|8|4|\n",
    "|control 3|4|5|\n",
    "|treated  |2|10|\n",
    "\n",
    "Since there are 3 units and only 2 attributes 2 match, there are multiple exact solutions to this problem, but a nice one is multiplying the first control by 2.25, multiplying the second by -2 and adding both. Notice how the second multiplication creates a fake unit with sales of -16 and price of -8. This multiplication is extrapolating the control 2 unit to a region of the data that doesn't make a lot of sense, since negative price and sales are almost impossible. The first multiplication is also an extrapolation, since it takes the first unit to a region where sales and price are 18. These numbers are much higher than anything we have in our data, hence the extrapolation.\n",
    "\n",
    "This is what regression is doing when we ask it to create a synthetic control. Extrapolation is not technically wrong, but it's dangerous in practice. We are making assumptions that the data we have never seen behaves like the data that we have. \n",
    "\n",
    "One way to play safer is to constrain our synthetic control to only do interpolation. To do so, we will restrict the weights to be positive and sum to one. Now, the synthetic control will be a convex combination of the units in the donor pool. When doing interpolation, we will project the treated unit in the convex hull defined by the untreated unit, much like in the picture below.\n",
    "\n",
    "![img](data/img/synth-control/extrapolation.png)\n",
    "\n",
    "Notice 2 things here. First, interpolation won't be able to create a perfect match of the treated unit in this case. This is because the treat is the unit with the smallest number of sales and the highest price. Convex combinations can only replicate exactly features that are in between the control units. Another thing to notice is that interpolation is sparse. We will project the treated unit on a wall of the convex hull and this wall is defined only by a few units. For this reason, interpolation will assign weight zero to many of the units. \n",
    "\n",
    "This is the general idea, now let's formalize it a little bit. The synthetic control is still defined as \n",
    "\n",
    "$\n",
    "\\hat{Y}^N_{jt} = \\sum^{J+1}_{j=2} w_j Y_{jt}\n",
    "$\n",
    "\n",
    "but now, we will use weights \\\\(\\pmb{W}=(w_2, ..., w_{J+1})\\\\) that minimises\n",
    "\n",
    "$\n",
    "||\\pmb{X}_1 - \\pmb{X}_0 \\pmb{W}|| = \\bigg(\\sum^k_{h=1}v_h \\bigg(X_{h1} - \\sum^{J+1}_{j=2} w_j X_{hj} \\bigg)^2 \\bigg)^{\\frac{1}{2}}\n",
    "$\n",
    "\n",
    "subject to the restriction that \\\\(w_2, ..., w_{J+1}\\\\) are positive and sum to one. Notice that \\\\(v_h\\\\) reflect the importance of each variable when minimising the difference between the treated and the synthetic control. Different \\\\(v\\\\)s would give different optimal weights. One way to choose \\\\(V\\\\) is to make it so that each variable has mean zero and unit variance. A more complex way is to choose \\\\(V\\\\) in such a way that variables that help to predict \\\\(Y\\\\) better get higher importance. Since we want to keep the code simple, we will simply give the same importance for each variable.\n",
    "\n",
    "To implement this, first, define the above loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from operator import add\n",
    "from toolz import reduce, partial\n",
    "\n",
    "def loss_w(W: np.array, treated: np.array, controls: List[np.array], V:np.array) -> float:\n",
    "    diff = treated - reduce(add, [i * w for i, w in zip(controls, W)])\n",
    "    return np.sqrt(np.mean(diff**2)) # I'm using the mean instead of the sum, but it doesn't matter much\n",
    "\n",
    "def loss_w(W, X, y) -> float:\n",
    "    return np.sqrt(np.mean((y - X.dot(W))**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using the same importance for every feature, we don't need to worry about v.\n",
    "\n",
    "Now, to get the optimal weights, we will use the quadratic programming optimisation of scipy. We will constrain the weights to sum to 1 with \n",
    "\n",
    "```python \n",
    "lambda x: np.sum(x) - 1\n",
    "```\n",
    "\n",
    "Also, we will set optimization bounds to be between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import fmin_slsqp\n",
    "\n",
    "def get_w(X, y):\n",
    "    \n",
    "    w_start = [1/X.shape[1]]*X.shape[1]\n",
    "\n",
    "    weights = fmin_slsqp(partial(loss_w, X=X, y=y),\n",
    "                         np.array(w_start),\n",
    "                         f_eqcons=lambda x: np.sum(x) - 1,\n",
    "                         bounds=[(0.0, 1.0)]*len(w_start),\n",
    "                         disp=False)\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this implemented, let's get the weights that define the synthetic control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum: 1.000000000000173\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.    , 0.    , 0.    , 0.0852, 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.113 , 0.1051, 0.4566, 0.    , 0.    ,\n",
       "       0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    , 0.    ,\n",
       "       0.2401, 0.    , 0.    , 0.    , 0.    , 0.    ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calif_weights = get_w(X, y)\n",
    "print(\"Sum:\", calif_weights.sum())\n",
    "np.round(calif_weights, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, with this weight, we are multiplying states 1,2, and 3 by zero, state 4 by 0.0852 and\n",
    "so on. Notice how the weights are sparse, exactly as we've predicted. Also, all weights sum to one and are between 0 and 1, satisfying our convex combination constraint.\n",
    "\n",
    "Now, to get the synthetic control, we can multiply those weights by the states exactly as we did before with the regression weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "calif_synth = cigar.query(\"~california\").pivot(index='year', columns=\"state\")[\"cigsale\"].values.dot(calif_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the outcome of the synthetic control now, we get a much smoother trend. Also note that the synthetic control doesn't reproduce the treated exactly in the pre intervention period. This is a good sign, as it indicates that we are not overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"], label=\"California\")\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], calif_synth, label=\"Synthetic Control\")\n",
    "plt.vlines(x=1988, ymin=40, ymax=140, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.ylabel(\"Per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the synthetic control at hand, we can estimate the treatment effect as the gap between the outcome in the treated and the synthetic control outcome.\n",
    "\n",
    "$\n",
    "\\tau_{1t} = Y^I_{jt} - Y^N_{jt}\n",
    "$\n",
    "\n",
    "In this case, the effect gets bigger and bigger as time passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "         label=\"California Effect\")\n",
    "plt.vlines(x=1988, ymin=-30, ymax=7, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=2)\n",
    "plt.title(\"State - Synthetic Across Time\")\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the year 2000, it looks like Proposition 99 has reduced the sales in cigarettes by 25 packs. That is very cool and all, but something you might be asking yourself if: how can I know if this is statistically significant?\n",
    "\n",
    "## Making Inference\n",
    "\n",
    "Since our sample size is very small (39), we will have to be a bit smarter when figuring out if our result is statistically significant and not just due to random luck. Here, we will use the idea of Fisher's Exact Test. It's intuition is very simple. We permute the treated and control exhaustively. Since we only have one treated unit, this would mean that, for each unit, we pretend it is the treated while the others are the control. \n",
    "\n",
    "|iteration|1|2|...|39|\n",
    "|----|-|-|-|-|\n",
    "|1|treated|0|0|0|\n",
    "|2|0|treated|0|0|\n",
    "|...|0|0|0|0|0|0|\n",
    "|39|0|0|0|treated|\n",
    "\n",
    "In the end, we will have one synthetic control and effect estimates for each state. So what this does is it pretends the treatment actually happened for another state, not California, and see what would be the estimated effect for this treatment that didn't happen. Then, we see if the treatment in Califórnia is sufficiently larger when compared to the other fake treatment.\n",
    "\n",
    "To implement this, I've built this function that takes as input a state and estimate the synthetic control for that state. This function returns a data frame with one column for the state, one for the year, one for the outcome `cigsale` and the synthetic outcome for that state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def synthetic_control(state: int, pool: List[int], data: pd.DataFrame) -> np.array:\n",
    "    \n",
    "    features = [\"cigsale\", \"retprice\"]\n",
    "    \n",
    "    inverted = (data.query(\"~after_treatment\")\n",
    "                .pivot(index='state', columns=\"year\")[features]\n",
    "                .T)\n",
    "    \n",
    "    y = inverted[state].values # treated\n",
    "    X = inverted.drop(columns=state).values # donor pool\n",
    "\n",
    "    weights = get_w(X, y)\n",
    "    synthetic = (data.query(f\"~(state=={state})\")\n",
    "                 .pivot(index='year', columns=\"state\")[\"cigsale\"]\n",
    "                 .values.dot(weights))\n",
    "\n",
    "    return (data\n",
    "            .query(f\"state=={state}\")[[\"state\", \"year\", \"cigsale\", \"after_treatment\"]]\n",
    "            .assign(synthetic=synthetic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the result of it when we apply it to the first state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>after_treatment</th>\n",
       "      <th>synthetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>False</td>\n",
       "      <td>95.029419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>False</td>\n",
       "      <td>99.118199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>101.881329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>False</td>\n",
       "      <td>103.938655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>False</td>\n",
       "      <td>107.038473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  year     cigsale  after_treatment   synthetic\n",
       "0      1  1970   89.800003            False   95.029419\n",
       "1      1  1971   95.400002            False   99.118199\n",
       "2      1  1972  101.099998            False  101.881329\n",
       "3      1  1973  102.900002            False  103.938655\n",
       "4      1  1974  108.199997            False  107.038473"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_pool = cigar[\"state\"].unique()\n",
    "\n",
    "synthetic_control(1, control_pool, cigar).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the result for all the state, we parallelize the computation across 8 processes. If your computer has more or less cores, you can use a different number. This code will return a list of data frames like the one above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "\n",
    "parallel_fn = delayed(partial(synthetic_control, pool=control_pool, data=cigar))\n",
    "\n",
    "sinthetic_states = Parallel(n_jobs=8)(parallel_fn(state) for state in control_pool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>after_treatment</th>\n",
       "      <th>synthetic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>False</td>\n",
       "      <td>95.029419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>False</td>\n",
       "      <td>99.118199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>101.881329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>False</td>\n",
       "      <td>103.938655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>False</td>\n",
       "      <td>107.038473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  year     cigsale  after_treatment   synthetic\n",
       "0      1  1970   89.800003            False   95.029419\n",
       "1      1  1971   95.400002            False   99.118199\n",
       "2      1  1972  101.099998            False  101.881329\n",
       "3      1  1973  102.900002            False  103.938655\n",
       "4      1  1974  108.199997            False  107.038473"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinthetic_states[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the synthetic control for all the states, we can estimate the gap between the synthetic and the true state for all states. For California, this is the treatment effect. For the other states, this is like a placebo effect, where we estimate the synthetic control treatment effect where the treatment didn't actually happen. If we plot all the placebo effects along with the California treatment effect, we get the following figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "for state in sinthetic_states:\n",
    "    plt.plot(state[\"year\"], state[\"cigsale\"] - state[\"synthetic\"], color=\"C5\",alpha=0.4)\n",
    "\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "        label=\"California\");\n",
    "\n",
    "plt.vlines(x=1988, ymin=-50, ymax=120, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=3)\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.title(\"State - Synthetic Across Time\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two aspects of this figure jump to the eyes. First, we can see that the variance after the intervention is higher than the variance before the intervention. This is expected, since the synthetic control is designed to minimize the difference in the pre-intervention period. Another interesting aspect is that there are some units we can't fit very well even in the pre-intervention period. This is also to be expected. For example, if some state have very high cigarette consumption, no convex combination of the other states will ever match it. \n",
    "\n",
    "Since those units are so poorly fit, it is a good idea to remove them from the analysis. One way to do it objectively is to set a threshold for pre-intervention error \n",
    "\n",
    "$\n",
    "MSE = \\frac{1}{N}\\sum\\bigg(Y_t - \\hat{Y}^{Synth}_t\\bigg)^2\n",
    "$\n",
    "\n",
    "and remove those usints with high error. If we proceed like this and plot the same figure, this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pre_treatment_error(state):\n",
    "    pre_treat_error = (state.query(\"~after_treatment\")[\"cigsale\"] \n",
    "                       - state.query(\"~after_treatment\")[\"synthetic\"]) ** 2\n",
    "    return pre_treat_error.mean()\n",
    "\n",
    "plt.figure(figsize=(12,7))\n",
    "for state in sinthetic_states:\n",
    "    \n",
    "    # remove units with mean error above 80.\n",
    "    if pre_treatment_error(state) < 80:\n",
    "        plt.plot(state[\"year\"], state[\"cigsale\"] - state[\"synthetic\"], color=\"C5\",alpha=0.4)\n",
    "\n",
    "plt.plot(cigar.query(\"california\")[\"year\"], cigar.query(\"california\")[\"cigsale\"] - calif_synth,\n",
    "        label=\"California\");\n",
    "\n",
    "plt.vlines(x=1988, ymin=-50, ymax=120, linestyle=\":\", lw=2, label=\"Proposition 99\")\n",
    "plt.hlines(y=0, xmin=1970, xmax=2000, lw=3)\n",
    "plt.ylabel(\"Gap in per-capita cigarette sales (in packs)\")\n",
    "plt.title(\"Distribution of Effects\")\n",
    "plt.title(\"State - Synthetic Across Time (Large Pre-Treatment Errors Removed)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the noise, we can see how extreme of a value is the effect in the state of California. This image shows us that if we pretend the treatment had happened to any other state, we would almost never get an effect so extreme as the one we got with California.\n",
    "\n",
    "This picture alone is a form of inference, but we can also derive a P-value from these results. All we have to do is see how many times the effects that we've got is below the effect of California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "California Treatment Effect for the Year 2000 -24.830159734493144\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  5.79715889,   0.89458996, -24.83015973,  -7.16628124,\n",
       "       -10.92204853,  37.1164056 , -15.06971531,  -0.49805123,\n",
       "       -18.45795059,  21.13366451,  12.57782765,  -1.47547827,\n",
       "        10.49627367, -11.67012352,   4.29850816,   8.04811405,\n",
       "        14.02322411,   8.2500273 ,   0.32576356,  -8.40826887,\n",
       "        -2.12402716,  -7.4286503 ,   2.96157551,  24.10478098,\n",
       "         4.25211766, -17.75844593,   7.93334015,   2.81640129,\n",
       "        12.64955979, -17.47677516, -25.16040937, -12.26469123,\n",
       "        24.69067377,  10.36299584,  -8.5988036 ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calif_number = 3\n",
    "\n",
    "effects = [state.query(\"year==2000\").iloc[0][\"cigsale\"] - state.query(\"year==2000\").iloc[0][\"synthetic\"]\n",
    "           for state in sinthetic_states\n",
    "           if pre_treatment_error(state) < 80] # filter out noise\n",
    "\n",
    "calif_effect = cigar.query(\"california & year==2000\").iloc[0][\"cigsale\"] - calif_synth[-1] \n",
    "\n",
    "print(\"California Treatment Effect for the Year 2000\", calif_effect)\n",
    "np.array(effects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if we want to test the one sided hypothesis that the effect in California is below zero, we can estimate the P-value as the proportion of time the effect in California is bigger than all the estimated effects.\n",
    "\n",
    "$\n",
    "PV=\\frac{1}{N}\\sum \\mathcal{1}\\{\\hat{\\tau}_{Calif} > \\hat{\\tau}_j\\}\n",
    "$\n",
    "\n",
    "As it turns out, the treatment effect for California in the year 2000 is -24.8, meaning that the intervention reduced the consumption of cigarettes by almost 25 packs. Out of all the other 34 placebo effects that we've estimated, only one is higher than the effect we found in California. So the p-value would be 1/35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02857142857142857"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array(effects) < calif_effect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can show the distribution of effects just to get a sense of how extreme the value of the effect in California really is. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa8AAAEGCAYAAADSeBonAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de5gcVZ3/8fcndyQQYIiQhShhGQR03cgiIKBGRCAQYVVg440guAIriKKrBPghxocV0BVXue2zgkSBAApIwCiigAl3CAQIJtAB0UQCAxOZMHIJId/fH1UTOp2emb5Nddfk83qefqbr1KlT31PdM9+pUzdFBGZmZnkypNkBmJmZVcvJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy1qOpDMlLRmgtidJCknblpsegPUdJWn1QLRdC0njJf1e0t8l1XWdTLrtFkp6XdLtadk/SbpP0quSnm5EzGblOHlZJiRdliaJkLRa0gpJd0v6pqQtSqp/D9iziraXSDqzwup3AeOAZyptv8IYtk37Nqlk1tXANo1cV51OBd4KTCTZDuspSujlXocVVb0IeBDYHvh4WnYusBLYCXhvIwKW9Jl6E60NPsOaHYBtUOYBR5D807Q5sAfwdeA4SR+MiCcAIqIb6G70yiWNiIhVwLONbrs3EfEK8EpW66tAO3BfRBQqqLsrsLyk7G8lbf1XRCwtKZsZEU/XFaVZfyLCL78G/AVcBvyuTPmmwJPArUVlZwJLiqa3Ba4FXiBJBE8B/5nOux2Iktd2wKT0/cHAHcCrwAlF5dumy/dMfxS4L633GPCRovWvs0xR+WrgqPR9aQxPp+VHAatLljsImA+8BnQAFwIbl24r4AvAn0n2ZG4AxvazjTcB/hd4Pu3HA8D+RfNLY7ysl3bK9rfM/OLXUWXKzkzrb5X26XngJeBO4AMlbf4j8HNgBfAy8AgwpZd1XZYus0/a1kvp62HggGZ/1/3K5uVhQ2uqiFhJMvw0SdLYXqpdCIwB9gN2Bo4BlqXzPg48Dfw3yTDYOKB4T+C/SYaydgZ+2Uco3wdmAO8B7gFmS6pmuG/X9Ocn0hjKDplJejcwG5hLMnQ3jeSP9MUlVd8LfIgk+R6Y1v1ePzFcChwAfCbtx53ATZJ2SuePA+4Grkzfn1RZ19bTM/QKyT8E40gSzziSz+Wc9P33JG0E3EaSWCencc0BbpG0M4CkrdM2NwcOAf4J+H/AmrT8hKL4xwEnSRpKsh3vJdn2u5L80/NyjX2ynPGwobWChYCACST/nZd6O3B9RCxIp5/umRERKyS9AXRHxNrhQEk9b8+KiNlF5Tv0EsPZEXFTWudYkkR5PHB6hX3oiXtFcRxl/CfwYER8JZ1eJOlE4HpJp0fEn9PyVSR7da+lMV1EH8km7ddhwMERcXNafJKk95MMzR4dEc9KWgW80k+MPR4vc6xpx4h4Bng23cZdRW39vfSzkHQUyd71v0VEz4krZ0n6MHAs8GXgiyR7VIdGxN/TOk8W9a0LoOTz3Zwk2c2ON4dAKxkKtUHCyctaQU+m6e2g/A+A/5U0mWSY8FcRMbfCtu+rsN7dPW8iYrWk+4BdKly2Gu8Ebi0p+wPJNtiFZJgQYFFP4kr9lWT4rTc9sZZul7nA+2oLlQNY//jgc1W28V5ga+DFon8oAEby5rHAfwHuKkpc/YqIv0n6MXCzpFtJtuH1EfF4lfFZTnnY0FrBu0gS11PlZkbET0j2vi4mGTb6taTLK2y74j+IJYr/0q4pLUuHrWr9/ektSReXryozT1RPfayvP09HxJKS1xtVtjEEWEQy7Fn82hn496J6VccYEf9OkvhuAT4ILEz3mm0D4ORlTSVpU5Lhud9HRGdv9SJieUT8JCKOJDnm9el0WUj+0A+tM5S1p+ZLGkayx7AoLepIf/5DUf2JrJtMepJNf3E8RvKHttgHSf54/7GKeMu1C/CBkvL3F81rhgdITqVfWSYR9lyuMB/YW9LGvbSxCtb+w7COiFgYEd+PiMnAJSQnudgGwMnLsjRC0taSxknaRdLRJMN6I0kSWFmSzpd0kKR/lPROkpM0lpKcYQbwJ5I/fm+TtKWkWr7Xp6Tr2JnkBJKt0p8AS0iG886UtJOkfYDzWHdv4QWS0/v3T/u4eS/r+S6wq6Tvp20dCPwIuCIi/lJD3ABExJMkJ01cKOmAtO3/Idmr/W6NzY5N+1L8ekuVbVxB8vn8StL+kraTtIek6ZL+Na1zIcnfohsk7S1pgqQp6TAx6fIAh0gaK2m0pB0knSNpH0lvl/Q+kkRdzz8AliNOXpal95NcN7SU5Ey4L5Cc+fauiOjrjhoiOe61kOQYzsbA5IjoSR7fJDkb8XGSEyfeVkNsXwO+DSwA9iY5eWAZJMfAgH8jubj3IeAC4DTeHE4kItaQnHhwRNq/h8qtJCIeITmj7oMkp3b/DPgVcFwNMZf6PHAzcHna9t7AlIhYXGN7D5J8XsWv/6imgYh4laSvDwA/AZ4ArgN2Jz2+FxHLSU57f4nkTMTHgLNI92wj4n7gf0iGjZ8DzicZDm4HrkrbvJZ1z0y0QU5v/v6bmZnlg/e8zMwsd5y8zMwsd5y8zMwsd5y8zMwsd3J1h42uri6fXWJmNsiNGTOm3wvyvedlZma54+RlZma54+SVgUJh8N7s2n3LJ/ctfwZrv6C2vjl5mZlZ7jh5mZlZ7uTqbEMzs2aJCLq7u1mzZk3/lQfAqFGj6Orqasq6G23IkCGMHj26+KGxVXPyMjOrQHd3NyNHjmTEiBFNWf/IkSMZNWpUU9bdaKtWraK7u5tNNtmk5jYyHTaUNFTSQ5JuKjNvpKSrJS2RdK+k7bKMzcysL2vWrGla4hpsRowYUfcebNbHvE7izQf8lToG+FtE7EDyrKRzMovKzMxyJbPkJWlb4GDgx71UORSYmb7/BfBh1TMgamY2yDz33HMcffTRTJw4kT322IPDDz+cJUt6fxTeNttsA8Dy5cs58sgj15Yfc8wx7LXXXlxwwQV1x/TQQw/x9a9/ve52qpXZ87wk/QL4DrAJ8LWImFIyfyFwYM8DACU9CewRES/01Cm+PdRgvuYhj56YWevzDt+047SdGhBJ6/A2GVxGjRrF2LFj105vPauzoe0/+8m2PudHBFOmTOGII45g2rRpACxcuJDu7m723HPPsstsv/32PPXUU+uUdXR0MHnyZObPn19xbKtXr2bYsMaeIvH888/z6quvrp1ub29f+76S20NlcsKGpClAR0TMlzSpt2plynrNrMUdbXWFQiFX8Vajp2+dbR11t9Vq26jez62Vt8mG8J1stK6urgE9YaK/tn/3u98xYsQIjj322LVlu+22G93d3RxxxBG8+OKLrF69mtNOO42DDz4YAEmMGjWKP//5z0ydOpW7776bqVOn0tnZyX777ce5557L6NGjOfnkk3n55ZeZMGECF1xwAZttthkHH3wwe+yxB/fccw+TJ0/mN7/5Dbvtthvz5s2jq6uLH/3oR+y1117MmzeP888/n6uvvpr58+czffp0XnnlFTbaaCMuuOCCXj+LTTfdlPHjxwOtfZHy3sAhkp4meWz3vpIuL6mzDBgPIGkYyWPdV2QUn5lZS1u8eDETJ05cr3zUqFFcfvnlzJ07lxtvvJHTTz+dvkbUZs2axYQJE7jjjjvYa6+9OO644zjzzDO566672GWXXTj77LPX1u3q6mLOnDmceOKJQLIHduutt/Kd73yHc85Z/7SE9vZ25syZw7x58zj11FOZMWNGA3peXiZ7XhExHZgOkO55fS0iPlNSbTYwDbgbOAy4NbIa0zQzy6mI4Nvf/jZ33nknQ4YMYfny5XR0dLDVVlv1u2xXVxcrV65kn332AeBTn/rU2iFJgI997GPr1P/oRz8KwMSJE/nLX/6yXnsrV67k+OOP56mnnkISr7/+ej1d61NT77AhaYakQ9LJS4A2SUuAk4FTmheZmVlrecc73sGCBQvWK7/mmmt44YUX+MMf/sAdd9zB2LFj1zmWVI+NN954nemRI0cCMHToUFavXr1e/bPOOov3v//93H333cyaNathcZSTefKKiNt7TtaIiDMiYnb6/tWIODwidoiI3SPiqb5bMjPbcOyzzz6sWrWKmTNnri178MEHWbp0KVtuuSXDhw9n7ty5LF26tOI2x4wZw5gxY7jrrrsAuOqqq9h7771rjnHlypWMGzcOgCuvvLLmdirhexuameWAJC6//HJuu+02Jk6cyJ577snZZ5/N/vvvz4IFC5g0aRI///nP2XHHHatq96KLLuKMM85gr7324tFHH+Ub3/hGzTGedNJJzJgxgwMOOIA33nij5nYqkdmp8o2Q1ycpbwhndt1z3p11t7XnV2r/j28g1Pu5tfI22RC+k43W1dXFmDFjGt5upV599dVBc3soWHd7ln5mfpKymZkNSk5eZmaWO05eZmaWO05eZmaWO05eZmYVGDJkCKtWrWp2GIPCqlWrGDKkvvTjh1GamVVg9OjRdHd388orrzRl/StXrmTTTTdtyrobredJyvVw8jIzq4Ckup78W6+Ojo61N7I1DxuamVkOOXmZmVnuOHmZmVnuOHmZmVnuOHmZmVnuOHmZmVnuOHmZmVnuZJK8JI2SdJ+khyU9JulbZeocJel5SQvS1+eziM3MzPInq4uUXwP2jYhuScOBOyT9OiLuKal3dUSckFFMZmaWU5kkr0ieeNmdTg5PX7l8sKSZmTVfZse8JA2VtADoAG6JiHvLVPuEpEck/UKS74NiZmZlKdkpynCF0mbA9cCJEbGwqLwN6I6I1yQdBxwREfsWL9vV1bU22EKhkFXIVoEnZi6uu40dp+3UgEhah7eJWeXa29vXvh8zZoz6q5/5jXkj4kVJtwMHAguLyjuLqv0fcE5f7RR3tNUVCoVcxVuNnr51tnXU3VarbaN6P7dW3iYbwndysBms/YLadkayOttwbLrHhaSNgP2AxSV1xhVNHgIsyiI2MzPLn6z2vMYBMyUNJUmY10TETZJmAA9ExGzgS5IOAVYDK4CjMorNzMxyJquzDR8B3lOm/Iyi99OB6VnEY2Zm+eY7bJiZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe44eZmZWe5kkrwkjZJ0n6SHJT0m6Vtl6oyUdLWkJZLulbRdFrGZmVn+ZLXn9Rqwb0T8MzAROFDSniV1jgH+FhE7AOcB52QUm5mZ5UwmySsS3enk8PQVJdUOBWam738BfFiSsojPzMzyJbNjXpKGSloAdAC3RMS9JVW2AZYCRMRqoAtoyyo+MzPLD0WU7gAN8AqlzYDrgRMjYmFR+WPAARGxLJ1+Etg9Ijp76nR1da0NtlAoZBf0AHli5uK629hx2k4tEUcjDKa+tJJW2a6NiMMGr/b29rXvx4wZ0++o27ABjaaMiHhR0u3AgcDColnLgPHAMknDgDHAit7aKe5oqysUCmXj7WzrqLvtRmyHeuLo7FxBW9sWdccAze/Lem01sG/NVG679vad7E2rfFcrUW3f8mKw9gtq2xnJ6mzDsekeF5I2AvYDSv+Vmw1MS98fBtwaWe8WmplZLmS15zUOmClpKEnCvCYibpI0A3ggImYDlwA/k7SEZI9rakaxmZlZzmSSvCLiEeA9ZcrPKHr/KnB4FvGYmVm++Q4bZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWO05eZmaWOxUnL0lDa12JpPGSbpO0SNJjkk4qU2eSpC5JC9LXGeXaMjMzG1ZF3eWSZgE/i4gHqlzPauCrEfGgpE2A+ZJuiYg/ltSbFxFTqmzbzMw2MNUMG04G3gBuTPegTpX0tkoWjIjlEfFg+v4lYBGwTdXRmpmZUUXyioj5EXEySdL5CrAL8Gg6HHi0pI0raUfSdsB7gHvLzH6fpIcl/VrSOyuNzczMNizVDBsCEBFrJC0GFgN7kiSzTwPfk3RSRPyst2UljQauBb4cEStLZj8IvD0iuiUdBPwSaO+trUKhUG3oTVUu3s7OFQPSbrXqjaMR/YDW6MtAt9cMvW3XarZ3q3xXW3FdWRqs/apFxclL0ubAEcBngZ2Ba4AjI+KudP57gd8CZZOXpOEkieuKiLiudH5xMouIOZIulLRlRLxQrr329l7zWsspFApl4+1s66i77UZsh3ri6OxcQVvbFnXHAM3vy3ptNbBvzVRuu/b2nexNq3xXK1Ft3/JisPYLakvK1ex5LQNuA34I3BARrxXPjIj7Jd1QbkFJAi4BFkXE93upszXwXESEpN1JhjQ7q4jPzMw2ENUkr+0j4rm+KkTEUb3M2ptkj+1RSQvSslOBt6XLXQwcBhwvaTXwCjA1IqKK+MzMbANRTfL6nKTfR8T9PQXpHtKkiDi3rwUj4g5A/dQ5Hzi/injMzGwDVc2p8icBpddl/RH4cuPCMTMz6181yWsE8HpJ2SpgVOPCMTMz6181yWs+8B8lZceRnOJuZmaWmWqOeX0FuEXSZ4EngR2ArYCPDERgZmZmvak4eUXEY5J2BD4KbAtcB9wUEd0DFZyZmVk5Vd1hI01UswYoFjMzs4pUc4eNCcBZwERgdPG8iKjoBr1mZmaNUM2e15Ukx7q+Crw8MOGYmZn1r5rk9U5g74hYM1DBmJmZVaKaU+XnkjzKxMzMrKmq2fN6GrhZ0nXAs8UzIuKMRgZlZmbWl2qS18bAjcBwYPzAhGNmZta/aq7z+txABmJmZlapqq7zkrQzyaNLtoqIEyS9AxgZEY8MSHRmZmZlVHzChqTDSU7a2AY4Mi3eBCj7cEkzM7OBUs3ZhjOAj0TEccAbadnDwD83PCozM7M+VJO83kqSrACi6KefdmxmZpmq9pEony0pmwrc19+CksZLuk3SIkmPSTqpTB1J+qGkJZIekbRrFbGZmdkGpJoTNr4E/FbSMcDGkm4GdgT2r2DZ1cBXI+JBSZsA8yXdEhHFT2aeDLSnrz2Ai9KfZmZm66jmVPnFknYCpgA3AUup8JEoEbEcWJ6+f0nSIpITP4qT16HATyMigHskbSZpXLqsmZnZWtU+EuVl4Jp6VihpO5LbTN1bMmsbkoTYY1la5uRlZmbrqOaRKPPo5eSMiPhAhW2MBq4FvhwRK0tnl2u6t7YKhUIlqyzriZmLa162x47Tdqqqfrl4OztX1B1HPduhUXE0oh/QGn0Z6Paa4Ven31i2/Anq/z2oRiM+31ZcV5YGa79qUc2e149LprcGjgEur2RhScNJEtcVEXFdmSrLWPe2U9sCz/TWXnt7eyWrLauzraPmZWtZf6FQKFs/6zh6U08cnZ0raGvbou4YoPl9Wa+tBvat1TSjb434fCvR2+9b3g3WfkFtSbmaY14zS8skXQv8hOQasF5JEnAJsCgieruoeTZwgqSrSE7U6PLxLjMzK6eqY15l/BV4dwX19iY5zf5RSQvSslOBtwFExMXAHOAgYAnJwy59L0UzMyurmmNeR5cUvQX4OHBPf8tGxB2UP6ZVXCeAL1Yaj5mZbbiq2fMqvUD578BdwHmNC8fMzKx/1Rzz+tBABmJmZlapaoYNt6+kXkQ8VXs4ZmZm/atm2HAJb153Jda9BqvneFYAQxsQl5mZWa+quTHvMcBVwE7AqPTnlcAxETEkfTlxmZnZgKtmz+vbQHtEvJJOFyQdCzwBXNbowMzMzHpTzZ7XEGC7krK342FCMzPLWDV7XucBt0r6CckNdMcDR+FT5c3MLGPVnCr/XUmPAoeT3BV+OXB0RPxmoIIzMzMrp6LkJWkoybGtXZyszMys2So65hURbwBvkJxlaGZm1lTVHPP6AXCNpP8ieXzJ2uu8fGGymZllqd/kJWnriHgWOD8t2o91b7LrC5PNzCxTlQwbPgHQcyEyMLvoomRfmGxmZpmrJHmVPsrkgwMRiJmZWaUqSV5RMt3nc7nMzMwGWiUnbAyT9CHeTFpDS6aJiFsHIjgzM7NyKkleHcClRdOdJdMB9Pm4FEmXAlOAjoh4V5n5k4AbgD+lRddFxIwKYjMzsw1Qv8krIrZrwHouIzlb8ad91JkXEVMasC4zMxvkqrkxb80iYi6wIot1mZnZ4JdJ8qrQ+yQ9LOnXkt7Z7GDMzKx1VXOHjYH0IPD2iOiWdBDwS6C9rwUKhULNK+vsrH8nsNr1l6vfjDjKqTeORvQDWqMvA91eK8m6b434fFtxXVkarP2qRUskr4hYWfR+jqQLJW0ZES/0tkx7e5+5rU+dbR01L1vL+guFQtn6WcfRm3ri6OxcQVvbFnXHAM3vy3ptNbBvraYZfWvE51uJ3n7f8m6w9gtqS8otMWwoaWtJSt/vThJXZ3OjMjOzVpXJnpekWcAkYEtJy4BvAsMBIuJi4DDgeEmrgVeAqRFRenG0mZkZkFHyiohP9jP/fN688a+ZmVmfWmLY0MzMrBpOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljtOXmZmljuZJC9Jl0rqkLSwl/mS9ENJSyQ9ImnXLOIyM7N8ymrP6zLgwD7mTwba09cXgIsyiMnMzHIqk+QVEXOBFX1UORT4aSTuATaTNC6L2MzMLH9a5ZjXNsDSoullaZmZmdl6hjU7gJTKlEVfCxQKhZpXduBm29W8bI/7q1x/uXg7O/vaGa3Mr06/se426tWIfkB9n2mPRsUyUO21kqz71ojP94mZiyurR2X1mmnHaTtVvUwjtmGpSrdpX2rpS71aJXktA8YXTW8LPNPXAu3t7bWv7Y6/1r5sDesvFApl63e2ddQdR7N1dq6grW2LhrRV12eaauQ2bWTfWk0z+pbV55uXz63a7dHb35F6NeJ3pt64aknKrTJsOBs4Mj3rcE+gKyKWNzsoMzNrTZnseUmaBUwCtpS0DPgmMBwgIi4G5gAHAUuAl4HPZRGXmZnlUybJKyI+2c/8AL6YRSxmZpZ/rTJsaGZmVjEnLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzy53MkpekAyU9LmmJpFPKzD9K0vOSFqSvz2cVm5mZ5cuwLFYiaShwAfARYBlwv6TZEfHHkqpXR8QJWcRkZmb5ldWe1+7Akoh4KiJWAVcBh2a0bjMzG2Qy2fMCtgGWFk0vA/YoU+8Tkj4APAF8JSKWlqkDQKFQqCOct9SxbG3rL1e/s3NF3XG0gkb1o77PNNHobTpYPqNysu5blp9vHj63WrZHI7ZhqUZsq4GIqz9ZJS+VKYuS6RuBWRHxmqTjgJnAvr012N7eXns0d/y19mVrWH+hUChbv7Oto+44mq2zcwVtbVs0pK26PtNUI7dpI/vWaprRt6w+37x8btVuj97+jtSrEb8z9cZVS/LLathwGTC+aHpb4JniChHRGRGvpZP/B/xLRrGZmVnOZJW87gfaJU2QNAKYCswuriBpXNHkIcCijGIzM7OcyWTYMCJWSzoBuBkYClwaEY9JmgE8EBGzgS9JOgRYDawAjsoiNjMzy5+sjnkREXOAOSVlZxS9nw5MzyoeMzPLL99hw8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMcsfJy8zMciez5CXpQEmPS1oi6ZQy80dKujqdf6+k7bKKzczM8iWT5CVpKHABMBnYBfikpF1Kqh0D/C0idgDOA87JIjYzM8sfRcTAr0R6H3BmRByQTk8HiIjvFNW5Oa1zt6RhwLPA2CgKsKura+CDNTOzphozZoz6q5PVsOE2wNKi6WVpWdk6EbEa6ALaMonOzMxyJavkVS6Llu5FVVLHzMyMYRmtZxkwvmh6W+CZXuosS4cNxwAriitUsitpZmaDX1Z7XvcD7ZImSBoBTAVml9SZDUxL3x8G3BpZHJAzM7PcySR5pcewTgBuBhYB10TEY5JmSDokrXYJ0CZpCXAysN7p9Hkj6buSFkt6RNL1kjYrmjc9vSzgcUkHNDPOWkg6XNJjktZI2q1kXt771udlHXki6VJJHZIWFpVtIekWSYX05+bNjLFWksZLuk3SovS7eFJanvv+SRol6T5JD6d9+1ZaPiG9lKiQXlo0otmx1kLSUEkPSbopna6+XxHh1wC9gP2BYen7c4Bz0ve7AA8DI4EJwJPA0GbHW2XfdgbeAdwO7FZUnuu+AUPTmLcHRqR92aXZcdXRnw8AuwILi8rOBU5J35/S873M2wsYB+yavt8EeCL9/uW+fyTnAIxO3w8H7gX2BK4BpqblFwPHNzvWGvt3MnAlcFM6XXW/fIeNARQRv41krxPgHpJjfQCHAldFxGsR8SdgCbB7M2KsVUQsiojHy8zKe992B5ZExFMRsQq4iqRPuRQRcyk5dkzSn5np+5nAv2YaVINExPKIeDB9/xLJqM42DIL+RaI7nRyevgLYF/hFWp7LvknaFjgY+HE6LWrol5NXdo4Gfp2+r+TSgbzKe9/yHn8ltoqI5ZAkAOCtTY6nbukded5DsocyKPqXDq0tADqAW0hGBF4s+oc4r9/NHwBfB9ak023U0K+szjYctCT9Dti6zKzTIuKGtM5pwGrgip7FytRvuZNTKulbucXKlLVc3/qQ9/g3OJJGA9cCX46Ilck/8vkXEW8AE9Nj5deTDNWvVy3bqOojaQrQERHzJU3qKS5Ttd9+OXnVKSL262u+pGnAFODDkQ7oUtmlA03XX996kYu+9SHv8VfiOUnjImK5pHEk/9nnkqThJInrioi4Li0eNP0DiIgXJaHemPcAAAE/SURBVN1OcsxrM0nD0r2UPH439wYOkXQQMArYlGRPrOp+edhwAEk6EPgGcEhEvFw0azYwNb0Z8QSgHbivGTEOgLz3rZLLOvKu+LKUaUBve9EtLT1WcgmwKCK+XzQr9/2TNLbn7GRJGwH7kRzTu43kUiLIYd8iYnpEbBsR25H8bt0aEZ+mln41+6yTwfwiOVlhKbAgfV1cNO80kjHsx4HJzY61hr59jGQv5TXgOeDmQdS3g0jOXHuSZIi06THV0ZdZwHLg9fTzOobkGMPvgUL6c4tmx1lj3/YhGV56pOh37KDB0D/g3cBDad8WAmek5duT/DO4BPg5MLLZsdbRx0m8ebZh1f3K5Ma8ZmZmjeRhQzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzyx0nLzMzy53/DxP/ZdEd/vT/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, bins, _ = plt.hist(effects, bins=20, color=\"C5\", alpha=0.5);\n",
    "plt.hist([calif_effect], bins=bins, color=\"C0\", label=\"California\")\n",
    "plt.ylabel(\"Frquency\")\n",
    "plt.title(\"Distribution of Effects\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Ideas\n",
    "We've learned that if we only have aggregated level data on entities like cities or states, diff-in-diff won't allow us to do inference. Also, it has some other limitations, since it has to define a control unit and one single control unit might not be a very good representation of the counterfactual of the treated unit. \n",
    "\n",
    "To correct for that, we learned that we can build a synthetic control that combines multiple control units to make them resemble the treated unit. With this synthetic control, we were able to see what would have happened to our treated unit in the absence of a treatment. \n",
    "\n",
    "Finally, we saw how we could use Fisher's Exact Tests to do inference with synthetic control. Namely, we've pretended that the non-treated units were actually the treated and computed their effect. There was the placebo effect: the effects we would observe even without a treatment. Then, whe saw how extreme of a value the true treatment effect was compared to the placebo.\n",
    "\n",
    "## References\n",
    "\n",
    "I like to think of this entire book as a tribute to Joshua Angrist, Alberto Abadie and Christopher Walters for their amazing Econometrics class. Most of the ideas here are taken from their classes at the American Economic Association. Watching them is what is keeping me sane during this tough year of 2020.\n",
    "* [Cross-Section Econometrics](https://www.aeaweb.org/conference/cont-ed/2017-webcasts)\n",
    "* [Mastering Mostly Harmless Econometrics](https://www.aeaweb.org/conference/cont-ed/2020-webcasts)\n",
    "\n",
    "I'll also like to reference the amazing books from Angrist. They have shown me that Econometrics, or 'Metrics as they call it, is not only extremely useful but also profoundly fun.\n",
    "\n",
    "* [Mostly Harmless Econometrics](https://www.mostlyharmlesseconometrics.com/)\n",
    "* [Mastering 'Metrics](https://www.masteringmetrics.com/)\n",
    "\n",
    "Other important reference is Miguel Hernan and Jamie Robins' book. It has been my trustworthy companion in the most thorny causal questions I had to answer.\n",
    "\n",
    "* [Causal Inference Book](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n",
    "\n",
    "Finally, I'd also like to compliment Scott Cunningham and his brilliant work mingling Causal Inference and Rap quotes:\n",
    "\n",
    "* [Causal Inference: The Mixtape](https://www.scunning.com/mixtape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
